Optimal Analyses for Cohort Tables

Paul R. Yarnold

Optimal Data Analysis, LLC

Cross-classification tables may be created for one or more “cohorts”— groups of observations defined by a common event such as the year of one’s birth, graduation, employment, marriage, disease diagnosis or incarceration—and assessed at two or more points in time on one or more variables reflecting the substantive focus of the study. This article demonstrates exploratory maximum-accuracy evaluation of cohort, aging, and time effects for a standard cohort table.

View journal article

Using ODA to Ascertain if Stratification Yields Different Transition Matrices

Paul R. Yarnold

Optimal Data Analysis, LLC

When a first-order Markov model cannot be confirmed one approach is subdividing the sample into strata each having a distinct set of transition probabilities. This note demonstrates the use of ODA to assess whether stratification resulted in significantly different transition matrices.

View journal article

Using ODA to Confirm a First Order Markov Steady State Process

Paul R. Yarnold

Optimal Data Analysis, LLC

Sufficiently iterated over time periods a first order Markovian change process defined by a constant transition matrix yields a steady state. Consecutive transition matrices are compared by Goodman’s chi-square test to assess if a steady state has been achieved. This note demonstrates the analogous use of ODA to assess if such transition matrices differ.

View journal article

Comparative Accuracy of a Diagnostic Index Modeled Using (Optimized) Regression vs. Novometrics

Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

Diagnostic screening tests are used to predict an individual’s graduated disease status which is measured on an ordered scale assessing disease progression (severity of illness). Maximizing the predictive accuracy of the diagnostic or screening test is paramount to correctly identifying an individual’s actual score along the ordered continuum. The present study compares two approaches for mapping a statistical model to a diagnostic index in order to make accurate outcome predictions for individuals. The application involves a dataset composed of multiple biomedical voice measurements for 42 individuals with early-stage Parkinson’s disease, who completed a six-month trial of a device for remote symptom progression telemonitoring. For 16 voice measures, each treated as a main effect, ordinary least-squares regression is used to predict baseline motor impairment component score. ODA is used to maximize accuracy of the regression model when it is mapped to the diagnostic index, and results are compared with accuracy achieved by the novometric solution.

View journal article

Identifying Maximum-Accuracy Cut-Points for Diagnostic Indexes via ODA

Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

Maximizing the discriminatory accuracy of a diagnostic or screening test is paramount to correctly identifying individuals with vs. without the disease or disease marker. In this paper we demonstrate the use of ODA to identify the optimal cut-point which best discriminates between those with vs. without the disease (or marker) under study, for any diagnostic test. We illustrate this methodology using a dataset composed of a range of repeated biomedical voice measurements from 31 people, 23 with Parkinson’s disease (PD). A logistic regression model was used to estimate the probability that each observation was from a person with vs. without PD as a function of 22 voice measurement variables, entered in the model as main effects only. Five different methods for computing a diagnostic cut-point on estimated probability are compared.

View journal article

Reanalysis of the National Supported Work Experiment Using ODA

Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

Data from the National Supported Work (NSW) randomized experiment have been used frequently over the past 30 years to demonstrate the implementation of various non-experimental methods for drawing causal inferences about treatment effects. In the present study we reanalyze the NSW data using ODA and compare results with those estimated using t-tests. Statistical results were largely consistent between methods, however ODA found 22.2% (2 of 9) preintervention characteristics to be imbalanced. Given that ODA avoids assumptions required of parametric methods, and is insensitive to skewed data and outliers, ODA should be considered the preferred approach when evaluating data from randomized experiments.

View journal article